Farabio

**TLDR**:
In this release, we ship segmentation models. In each of their architectures, backbone part is shared so that every segmentation model can be used with different backbone.

**Architectures**:
- DeepLabV3
- U-Net
- LinkNet
- PSPNet
- FPN

**Backbones**:
- VGG
- ResNet
- MobileNetV2

**TLDR**:
This is a fresh, restructured release package compared to v0.0.1. Here, we ship several classification models and biodatasets in PyTorch friendly format.

**Models**:
- AlexNet
- GoogLeNet
- MobileNetV2
- MobileNetV3
- ResNet
- ShuffleNetV2
- SqueezeNet
- VGG

**Biodatasets**:
- ChestXrayDataset
- DSB18Dataset
- HistocancerDataset
- RANZCRDataset
- RetinopathyDataset

**TLDR**:
This is the very first release. In this release, we ship various baseline models for classification, segmentation, detection, super-resolution and image translation tasks. As well, basis for model trainers and biodatasets are described here. Architectures are not as clean. Please refer to new releases in the future.

**Biodatasets**:
- ChestXrayDataset
- DSB18Dataset
- HistocancerDataset
- RANZCRDataset
- RetinopathyDataset

**Trainers**:
- BaseTrainer
- ConvnetTrainer
- GanTrainer

**Models**:
- DenseNet
- GoogLeNet
- VGG
- ResNet
- MobileNetV2
- ShuffleNetV2
- ViT
- U-Net
- Attention U-Net
- FasterRCNN
- YOLOv3
- CycleGAN
- SRGAN